Fernando Martínez S.
Universidad Distrital Francisco José de Caldas

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Low cost, high performance fuel cell energy conditioning system controlled by neural network Fredy H. Martínez S.; Fernando Martínez S.; Holman Montiel Ariza
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.16426

Abstract

Fuel cells are an important option for the generation of renewable, efficient and environmentally friendly electricity. Although there are commercial applications in the industrial, residential and automotive sectors, it is not yet a mature technology and requires much research, particularly to reduce its costs to a level competitive with other technologies. This research is currently focused not only on the structure of the cell but also on the additional elements and sub systems required for its implementation as an energy solution. In this article, we propose an electrical energy conditioning scheme for the Formic acid fuel cell (direct formic acid fuel cellor DFAFC). This fuel cell was selected for its high performance, and low cost in low and medium power applications. The proposed system consists of a direct current-direct current (DC-DC) regulator supported by a power converter controlled by a Cortex-M3 ARM processor. This CPU is used to propagate a static neural network trained with the non-linear dynamics of the power converter. The power circuit is modeled and simulated to produce  the training parameters. The neural network is trained externally and runs off-line on the processor. The results show not only the regulation capacity of the control scheme but also its response speed to sudden changes in the load.
Design of a controller for wheeled mobile robots based on automatic movement sequencing Holman Montiel Ariza; Fredy H. Martínez S.; Fernando Martínez S.
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.16431

Abstract

There are many kinds of robots and among them the wheeled mobile robots (WMR) stand out, because they are relatively cheap and easy to build. These features make WMRs the test prototypes for control strategies or motion generation. In general, the controllers developed are based on sensory schemes that give an WMR the ability to travel through flat or obstructed environments. However, these strategies are highly reactive, i.e. they are based on the control-action scheme and are not adaptive; or, they are motion schemes built from simulations that assume the environmental conditions to determine the robot's path. In both cases, WMRs do not adapt perfectly to the change of environment, since the controller does not find appropriate movements for the  robot to move from one point to another. Therefore, this article proposesapartial solution to this problem, with a controller that generates sets of adaptive movements for an WMR to travel around its environment from the sensory perception information.
Bacterial quorum sensing applied to the coordination of autonomous robot swarms Fredy H. Martinez S.; Fernando Martinez S.; Holman Montiel A.
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.546 KB) | DOI: 10.11591/eei.v9i1.1538

Abstract

This paper proposes a strategy for the coordination of a swarm of robots in an unknown environment. The basic idea is to achieve the autonomous movement of the group from an initial region to a target region avoiding obstacles. We use a behavior model similar to bacterial Quorum Sensing (QS) as a technique for the coordination of robots. This behavior has been described as a key element in the interaction between bacteria, and we use it as a basic tool for local interaction, both between the robot and between the robot and the environment. The movement of the swarm of robots, or multi-agent robotic system, is shown as an emerging behavior resulting from the interaction of agents (in the context of artificial intelligence) from basic rules of behavior. The proposed strategy was successfully evaluated by simulation on a set of robots.